The present technology relates to a recognizing apparatus and method, a program, and a recording medium, and more particularly, to a recognizing apparatus and method, a program, and a recording medium, which are capable of further increasing a resistance characteristic against image loss, noise, and the like in techniques related to image recognition.
In the past, a contour feature quantity obtained by edge extraction has been used as a main feature quantity for detecting (recognizing) an object from an image. In this technique, various modifications of the contour feature quantity obtained by edge extraction are defined as a new feature quantity, and recognition of an object is performed.
Further, for example, a statistical learning by boosting has been used for various image recognition tasks as an efficient machine learning method. For example, a statistical machine learning method called AdaBoost has been known as a machine learning method.
In AdaBoost, a weak identifier (which is also referred to as a weak learner) is generated using a learning image including a target object that is desired to be detected and a learning image including no target object, and a strong identifier is constructed by combining many weak identifiers. Using the strong identifier obtained in the above-described way, a target object can be detected from any image.
Further, for example, a technique by which an object can be detected from an image using an integrated identifier when a feature of similarity of texture of two areas can be sufficiently extracted from the input image, even when it is difficult to sufficiently extract a contour from the input image, has been proposed (for example, see Japanese Patent Application Laid-Open No. 2009-140283).